I spent much of yesterday pouring my mental energy into designing a better AI-assisted code review process. Then I threw it away.

What I learned is that you shouldn’t build every idea you have. I thought I had found a great way to improve the code review process. When I tried to say exactly what the system would improve, the answer was: not much. It was too complicated for the improvement it offered over an agent skill. I didn’t notice this when I was designing it. I had to step away and stop thinking about it before I could see the full cost of building it.

OpenAI took the world by storm in 2023 with the relase of ChatGPT. Other companies quickly followed suit, introducing their own competitors to ChatGPT. In this update, I want to write down my thoughts on some of the biggest players in the field of generative AI.

When I use the term “generative AI,” I am referring to a computer program that can generate text, images, or other types of content. These programs can understand natural language and follow complex instructions. One notable example of a generative AI is OpenAI’s “GPT-4,” commonly known as ChatGPT. Another well-known generative AI is Midjourney, which specializes in generating images from text descriptions. In this update, I will focus on AI with text output, as that is the area I have the most experience with.

Once you know a programming language well, the process for learning a new language is not very hard. It is just time consuming. You need to read the documentation for basic syntax and flow control, get familiar with its idioms, memorize core parts of the standard libraries, and learn its tool chains. What can we do to speed up the learning process? One thing we can do is provide great examples in the documentation. Can we do better? What if you have working examples for every problem you encountered? What if you can describe your intents in a familiar language and see how it should look in a new language? As it turns out, GPT-3 is really good at this task.

I had two weeks off at the end of 2022. Telesign closed its operations on the last week of 2022 to give employees well-deserved time off for a year of hardwork and I took another week off in addition to that. Like many technologists, I became captivated by OpenAI’s release of ChatGPT in 2022, and I spent a lot of the last two weeks exploring what OpenAI has to offer.

ChatGPT is a chatbot developed by OpenAI. It has been widely recognized for its impressive capabilities, such as imitating human writing, transforming plain English into code, and making glaringly stupid mistakes. The underlying technology of ChatGPT is a machine learning model known as GPT-3. This model is designed to predict how a human might continue a previous piece of text. For example, given what I’ve written so far, GPT-3 predicted what the rest of this blog post will look like:

I used to favor automated integration testing. Now, I find myself walking away from it. I no longer find it worth the cost of setting up and maintaining such tests. I now rely mostly on unit testing.

First, I need to define what I meant by those terms. In context of this post, unit tests

In Wikipedia

From Martin Fowler

This Feburary, I ordered a Dell XPS-13 Developer Edition. The Developer Edition is a line of Dell laptops that ships with certified Linux OS. It has been my programming powerhouse for the last eight months.

My first choice for a laptop was not Linux. Windows and macOS are simply more practical. Games and MS Office just works on Windows. Software support on macOS is generally good (except games), but it beats Windows with its underlying BSD architecture. There is better hardware support simply because more people use it. However, I ultimately ended up with a Dell and Linux and it worked out great.

As usual, LWN has a good write up on what’s going on in the Go community. This week’s discussion in on the new io/fs package. The Go team decided to use a Reddit thread to host the conversation about this draft design. LWN points out that posters raised the following concerns:

• We added status logging by wrapping http.ResponseWriter, and now HTTP/2 push doesn’t work anymore, because our wrapper hides the Push method from the handlers downstream. / It becomes infeasible to use the decorator pattern more
• Doing it “generically” involves a combinatorial explosion of optional interfaces

Ultimately, Russ Cox admits, “It’s true - there’s definitely a tension here between extensions and wrappers. I haven’t seen any perfect solutions for that.”

It took me way to long to learn this. Your code (and their unit tests) should inject the system clock as a dependency.

An example, let’s say you have a service that writes a record to the database with the system clock.

public void save(String userName) {
	long currentTimeMs = System.currentTimeMillis();
	User user = User.builder()
	    .name(userName)
		.updateTimeMs(currentTimeMs);
	database.save(user);
}

How would you test this? You can inject a mock database instance and use it to verify that it got a User object. Great! You can verify the username is as expected. How do you verify that tricky business rule that updateTimeMS is the “current time”?

Here’s a rough layout of how I organize my Go project. Some parts are situational and some parts are essential. I’ll go over both in this blog.

A rough layout:

+ basedir
   +-- go.mod (module jcheng.org)
   +-- hello (empty)
         +-- log/
         +-- utils/
         +-- config/
         +-- models/
         +-- repositories/
         +-- services/
         +-- cmd/
              +-- hello_app/
                     +--/cmd/
                          +-- speak/
                          +-- email/
                          +-- sms/

The basedir

Situational.